tfp.math.fill_triangular  |  TensorFlow Probability (original) (raw)

Creates a (batch of) triangular matrix from a vector of inputs.

tfp.math.fill_triangular(
    x, upper=False, name=None
)

Created matrix can be lower- or upper-triangular. (It is more efficient to create the matrix as upper or lower, rather than transpose.)

Triangular matrix elements are filled in a clockwise spiral. See example, below.

If x.shape is [b1, b2, ..., bB, d] then the output shape is[b1, b2, ..., bB, n, n] where n is such that d = n(n+1)/2, i.e.,n = int(np.sqrt(0.25 + 2. * m) - 0.5).

Example:

fill_triangular([1, 2, 3, 4, 5, 6])
# ==> [[4, 0, 0],
#      [6, 5, 0],
#      [3, 2, 1]]

fill_triangular([1, 2, 3, 4, 5, 6], upper=True)
# ==> [[1, 2, 3],
#      [0, 5, 6],
#      [0, 0, 4]]

The key trick is to create an upper triangular matrix by concatenating xand a tail of itself, then reshaping.

Suppose that we are filling the upper triangle of an n-by-n matrix Mfrom a vector x. The matrix M contains n**2 entries total. The vector xcontains n * (n+1) / 2 entries. For concreteness, we'll consider n = 5(so x has 15 entries and M has 25). We'll concatenate x and x with the first (n = 5) elements removed and reversed:

x = np.arange(15) + 1
xc = np.concatenate([x, x[5:][::-1]])
# ==> array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 14, 13,
#            12, 11, 10, 9, 8, 7, 6])

# (We add one to the arange result to disambiguate the zeros below the
# diagonal of our upper-triangular matrix from the first entry in `x`.)

# Now, when reshapedlay this out as a matrix:
y = np.reshape(xc, [5, 5])
# ==> array([[ 1,  2,  3,  4,  5],
#            [ 6,  7,  8,  9, 10],
#            [11, 12, 13, 14, 15],
#            [15, 14, 13, 12, 11],
#            [10,  9,  8,  7,  6]])

# Finally, zero the elements below the diagonal:
y = np.triu(y, k=0)
# ==> array([[ 1,  2,  3,  4,  5],
#            [ 0,  7,  8,  9, 10],
#            [ 0,  0, 13, 14, 15],
#            [ 0,  0,  0, 12, 11],
#            [ 0,  0,  0,  0,  6]])

From this example we see that the resuting matrix is upper-triangular, and contains all the entries of x, as desired. The rest is details:

• If n is even, x doesn't exactly fill an even number of rows (it fillsn / 2 rows and half of an additional row), but the whole scheme still works.
• If we want a lower triangular matrix instead of an upper triangular, we remove the first n elements from x rather than from the reversedx.

For additional comparisons, a pure numpy version of this function can be found in distribution_util_test.py, function _fill_triangular.